1. Technical Field
This invention relates to a signal processing method and apparatus for detecting faults and other features of rotating components that slip in rotating machinery. In particular, it relates to a method and apparatus that employs shaft encoders to average synchronously and to isolate signals arising from these asynchronously rotating components.
2. Background
Rolling-element bearings ("roller bearings") are manufactured in greater numbers than any other precision machine component and their failure is the most common cause of machine breakdown. The ability to detect the onset of breakdown of the elements of a roller bearing, such as the onset of pitting or cracks in the bearing races or rolling elements is fundamental to practicing predictive maintenance on machinery and avoiding catastrophic machine failure.
Roller bearings, other anti-friction bearings, belt-pulley assemblies and various other machine components are known to experience rotational slip in ordinary operation. The rotors of AC induction motors may also be considered to slip relative to their rotating electromagnetic fields. Oil whirl and oil whip can be considered as slipping shaft motions relative to the shaft rotation in journal bearings. Fluid circulation about the rotating shaft in a journal bearing or mechanical seal can be considered as a rotating component slipping relative to the shaft motion.
Vibration-signal-processing methods, including power spectral techniques, have been used to analyze the operation of some of these slipping components. For anti-friction bearings, the presence of large spectral components at the (1) inner-race ball passing frequency, (2) outer-race ball passing frequency, (3) ball-spin frequency and (4) cage-rotation frequency, as well as the higher harmonics of each of these components, tend to indicate the existence of faults in the associated bearing components. However, these signals are difficult to distinguish from background noise. When faults are present, frequency components commonly exist that correspond to modulations, sidebands, or interactions between the foregoing slipping components and various shaft-rotation frequency harmonics. However, these modulations or sidebands generally have not been utilized in the spectral analysis of bearings.
Other methods, which examine impulsive excursions in the time signal or measure the amplitudes of higher frequency bands, e.g. shock pulses, stress waves and spike energy, are in use as well. However, all of these methods are adversely effected by background noise and can be difficult to implement in the presence of numerous interfering narrow-band noise components in the spectra. Also the false alarms generated by such methods due to the persistence of background noise is a strong deterrent to sales of predictive maintenance equipment.
A signal processing method called "shaft-synchronous averaging" has been used to analyze gear vibration and detect gear faults (Hernandez, et. al., Weichbrodt, McFadden). This method is based on the fact that gears are rigidly coupled to their associated shafts, and to each other by the intermeshing of their teeth. Because of the fixed rotational relationships of the elements in a gear machine, signals associated with their rotation, such as vibration, can be averaged synchronously, i.e. by super-position of the signals in fixed relationship to the angular orientation of one element of the assembly, such as a gear-driven shaft. For large numbers of data sets, this synchronous-averaging gear method produces spectra reflecting only those vibrations synchronous with gear rotation, and not such asynchronous vibrations as from bearings and other components that slip. It should be noted that for a machine containing synchronously rotating components, the simple subtraction of consecutive data records before signal conditioning and processing eliminates most of the synchronous signals, such as shaft harmonics. For the case of a complex gearbox all gear mesh and shaft generated signals can be eliminated by subtracting data records that are separated by an integer multiple of the hunting tooth cycle, the hunting tooth cycle being defined as the time required for all gears and shafts of interest to rotate to their initial angular orientation.
It is believed that no signal-processing technique has previously been reported for use with machine components that slip that is comparable in fault-detection capability to the known gear techniques described above. The current invention, however, achieves that goal. It embodies new techniques and a device to monitor rotating machines with rotating components that slip as a means to detect faults, and to forecast and prevent catastrophic machinery breakdown.